机载LiDAR矿区沉陷信息提取方法研究

发布时间：2018-04-13 10:44

本文选题：机载LiDAR + 差值DEM　；参考：《河南理工大学》2016年硕士论文

【摘要】：矿区沉陷监测对于保障矿产资源安全开采、保护矿区安全具有重要意义,已有的地面监测方法劳动强度大、效率低下、覆盖范围有限。机载LiDAR系统能够快速获取大面积地表高分辨率、高精度的三维点云数据。使用机载LiDAR技术对开采塌陷引起的地表形变进行监测,可以较快地获得整个区域的空间地形变化信息,从而确定矿区的沉陷分布与地表移动下沉情况。论文基于2009、2012年两期机载LiDAR点云数据,构建了研究区沉陷信息提取的技术流程。在对两期DEM差值信息不确定性分析的基础上,去除了非开采沉陷导致的地形变化信息,进而提取到研究区沉陷盆地的空间分布与沉陷量估算值。论文主要研究内容可概括为以下三个方面:(1)分析了机载LiDAR点云数据的特点,在采用渐进三角网算法对机载LiDAR点云作滤波处理的基础上,基于反距离加权插值法建立了两期高分辨率DEM数据;(2)针对机载LiDAR数据的特点,在对DEM精度与不确定性分析的基础上,利用模糊推理方法建立了基于坡度、点云密度和地表粗糙度的误差相关表面,用于差值DEM不确定性的量化与DEM最小变化阈值的探测,进一步采用基于权重滤波窗口的贝叶斯估计判定与修正,较精确地获取了研究区地表形变信息;(3)针对上述处理中存在地表侵蚀等导致的地形变化信息,论文在坡度相关性分析的基础上,通过掩膜去除了非开采沉陷导致的地形变化信息,并利用多项式拟合方法对剖面数据进行处理,获取了较为精确的沉陷盆地信息。通过实验研究我们发现,该方法适用于大面积开采沉陷监测,能够快速确定沉陷区位置,较准确获取沉陷区的分布范围、面积及体积等信息。但是,若地表植被覆盖度增加,提取的DEM误差将增大,高程差误差也随之增大。基于机载LiDAR数据,我们能够较准确地提取沉陷区的位置、分布、沉陷深度以及沉陷量等信息,这为开采沉陷损害评价及开采沉陷预测提供了一种新的技术手段。
[Abstract]:Mining subsidence monitoring is of great significance for ensuring the safe mining of mineral resources and protecting the safety of mining areas. The existing surface monitoring methods are of great labor intensity, low efficiency and limited coverage.Airborne LiDAR system can quickly obtain large area surface high resolution, high accuracy 3D point cloud data.Using airborne LiDAR technology to monitor the ground deformation caused by mining collapse, the spatial topographic change information of the whole area can be obtained quickly, and the subsidence distribution and subsidence of the mining area can be determined.Based on the two issues of airborne LiDAR point cloud data in 2009 and 2012, the technical process of extracting subsidence information in the study area is constructed in this paper.Based on the uncertainty analysis of DEM difference information in two periods, the topographic variation information caused by non-mining subsidence is removed, and then the spatial distribution and subsidence estimation value of subsidence basin in the study area are extracted.In this paper, the main research contents can be summarized as follows: 1) the characteristics of airborne LiDAR point cloud data are analyzed. Based on the progressive triangular network algorithm, the airborne LiDAR point cloud is filtered.Based on the inverse distance weighted interpolation method, two periods of high resolution DEM data are established. According to the characteristics of airborne LiDAR data, based on the analysis of the accuracy and uncertainty of DEM, the slope based on fuzzy reasoning method is established.The error correlation surface of point cloud density and surface roughness is used for quantization of difference DEM uncertainty and detection of DEM minimum change threshold. Furthermore, Bayesian estimation and correction based on weight filtering window are used.In view of the topographic change information caused by the surface erosion in the above processing, the paper analyzes the correlation of slope degree.The terrain change information caused by non-mining subsidence is removed by mask, and the profile data are processed by polynomial fitting method, and more accurate information of subsidence basin is obtained.Through experimental study, we find that this method is suitable for monitoring subsidence in large area mining, and can quickly determine the location of subsidence area, and obtain the information of distribution, area and volume of subsidence area accurately.However, if the vegetation coverage is increased, the extracted DEM error will increase, and the elevation error will also increase.Based on airborne LiDAR data, we can accurately extract the location, distribution, depth and amount of subsidence, which provides a new technical means for mining subsidence damage evaluation and mining subsidence prediction.
【学位授予单位】：河南理工大学
【学位级别】：硕士
【学位授予年份】：2016
【分类号】：TD327

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